System and method for distorted camera image correction

ABSTRACT

The invention provides an image processing method for processing a sequence of images, comprising the steps of: obtaining, from a visual sensor, at least two images of the sequence of images, detecting whether the images include a distortion, determining an image warping function at least partially compensating the distortion, applying the determined warping function to the image(s) including the distortion, and calculating by a processing unit, and outputting, an optical flow as a displacement vector field form the images.

The invention relates to an image processing method and an optical(visual) sensor system, especially a camera system, for determining anoptical flow resulting from an ego-motion of the camera system. Inparticular, the invention relates to a vehicle, especially a ground, airor sea vehicle, comprising the sensor system adapted todetermine/calculate the optical flow from images/an image streamprovided by an optical sensor.

The sensor system comprises at least one optical sensor, such as acamera (CCD, CMOS, a laser scanner, an infrared sensor, etc. The visualsensor produces images and sends these images to a processing unit, e.g.as a stream of images. The optical sensors can also, e.g., be a stereocamera.

The processing unit processes the images and derives image informationfrom the images provided by the at least one optical sensor. Theprocessing unit may be part of the optical sensor system, but may alsobe positioned remote from the sensor system. For example, an imagestream can be supplied from a camera-based stream recording system tothe processing unit for processing.

Egomotion refers to an estimation of a motion of the visual sensorwithin the environment while taking a sequence of images taken by thesame visual sensor. The process of estimating the visual sensor's motionwithin an environment can involve the use of visual odometry techniques(the process of determining the position and orientation of a system byanalyzing associated camera images) on a sequence of images captured bythe moving visual sensor. This is typically achieved using featuredetection to construct an optical flow from two image frames in an imagesequence or an image stream generated from one or more visual sensors.Using stereo image pairs for each (time) frame helps to reduce errorsand provides additional depth and scale information.

Features are detected in the first frame, and then matched in the secondframe. This information is then used to estimate the optical flow fieldfor the detected features in the two images. For forward motion alongthe camera axis, the optical flow field illustrates how features divergefrom a single point, the focus of expansion. The focus of expansion canbe detected from the optical flow field, indicating the direction of themotion of the camera, and thus providing an estimate of the cameramotion.

The optical flow, which basically is a displacement vector fielddenoting the motion direction and/or motion speed of all or a portion ofpixels in an image sequence, is computed by the processing unit bycorrelating pixels or image patches between consecutive images suppliedby the at least one visual sensor.

The correlations are determined by comparing pixels or patches from aprevious image with patches or pixels from another image followingtemporally after the previous image.

Document “Deqing Sun, Stephan Roth, Michael J. Black (2010). Secrets ofoptical flow estimation and their principles. IEEE Conf. on ComputerVision and Pattern Recognition.” provides an overview and review ofstate-of-the-art optical flow estimation.

Document “Thomas Brox, Andres Bruhn, Nils Papenberg, Joachim Weickert(2004). High Accuracy Optical Flow Estimation Based on a Theory forWarping. European Conference on Computer Vision.” describes a techniquefor implementing a coarse-to-fine strategy for gradient-based opticalflow techniques. Gradient-based optical flow techniques have the bigproblem that their search range is restricted to only a few pixels.Hence, only very small movements can be detected. To counteract thisproblem, the optical flow is computed on multiple image resolutions. Onthe small image resolution the apparent movements are very small so thatthe gradient-based optical flow methods can be applied. This informationis then propagated to the next resolution level by warping. Thus, thecoarse-to-fine warping enables gradient-based optical flow methods towork for large movements by reducing the lateral movement.

Determining the optical flow is e.g. especially important in the fieldof autonomous or partially autonomous vehicles, in which a vehicle 1, asschematically and exemplarily shown in FIG. 1, moves from a startingpoint to a destination without planned intervention by a passenger ofthe autonomous vehicle. On the movement path from the starting point tothe destination, the autonomous vehicle en route automatically adaptsits movement path to traffic conditions.

In order to perceive its environment, the autonomous vehicle typicallycomprises a number of sensors sensing the environment but at least avisual sensor system 2, which comprises at least a visual sensor.Basically, sensing in this case means that the autonomous vehicleprocesses data supplied by the sensors in a processing unit 3 to deriveparameters that symbolize aspects of the environment. Together, thederived parameters form a virtual model of the vehicle's view of theenvironment.

The autonomous vehicle 1 continuously monitors the parameters and makesdecisions based on the parameters, i.e. the result of a calculation orparameter comparison leads to a result which leads to an execution of aspecified process. A decision is made, when specific constraints orthresholds are reached by the parameters. While a number of differentdecisions can be made, for autonomous movement, decisions that lead tomovement changes of the vehicle are of interest, as they influence othertraffic participants.

For effecting movement changes, the vehicle 1 typically comprises atleast actuators for actuating steering, for accelerating or decelerating(braking) the vehicle and/or for communicating with the passengers.After a decision is made, i.e. a process is started, the autonomousvehicle 1 actuates the actuators in accordance with steps, calculationsand/or comparisons specified in the respective process.

At least some of the visual sensors can be cameras, which are used togenerate the image sequence for calculating the optical flow in order toenhance navigation and to avoid objects/obstacles in the movement pathof the autonomous vehicle 1.

To determine an action that is required if an obstacle/object isdetected in the movement path of the vehicle, it is important todetermine whether the object is a moving object or a static object. Forexample, in case of the autonomous vehicle being a car, moving objectstypically are other cars, trucks, motorcycles, bicycles, pedestrians,etc. As another example, for an autonomous lawn mower, moving objectscould be moving persons or animals.

When the optical flow resulting from the egomotion of the vehicle can becalculated, also the impact of moving objects in the data supplied fromthe visual sensor system 2 can be determined, and hence, moving objectscan be detected and their movement can be tracked.

When using a sequence of images to calculate the optical flow resultinge.g. from the ego-motion of a vehicle equipped with the visual sensorsystem, the problem exists that the optical flow cannot be reliablycalculated if images in the image sequence are distorted.

The invention hence provides a method and a system for improving theoptical flow calculation performance from images which includedistortions.

In one aspect, an image processing method for processing a sequence ofimages is provided, comprising the steps of: obtaining, from an opticalsensor (this includes multi-sensor systems such as stereo sensorsystems), at least two images of the sequence of images, optionallydetecting whether one of the images include a distortion, determining animage warping function that at least partially compensates a distortionin at least one of the images, applying the determined warping functionto the at least one image, at least partially compensating thedistortion by modifying the image, and calculating and outputting anoptical flow as a displacement vector field form modified image andanother image of the at least two images.

The flow field can be corrected by subtracting shifts introduced due tothe warping function application.

The optical flow can be calculated by matching patches of one image tothe other.

The warping function is determined from assumptions, i.e. pre-storeda-priori knowledge, on the scene that is recorded by the vision sensorand the knowledge about movement of the visual system.

The warping function may be determined from motion information, e.g.supplied by a sensor of the optical sensor, indicative of the motion thedistortion results from.

The distortion can be a motion distortion and/or a lens distortion.

More than one image warping function may be applied to the imageincluding the distortion.

The image warping function is applied to one of the at least two images.

The method can be performed more than once, each time with a differentimage warping function on the same images and the optical flow resultsmay be combined into one optical flow, i.e. a single displacement vectorfield.

In addition to the calculation of the optical flow from the images withapplication of the image warping function, a second optical flow can becalculated from the images without application of the image warpingfunction and both optical flow results can be combined.

The image warping function may be determined from a previous opticalflow calculation.

The image warping function can be a predetermined and fixed imagewarping function, an affine warping function, and/or a nomography.

Objects and/or moving objects in the images can be detected based on theoptical flow. This can be achieved e.g. by segmenting or clustering theoptical flow into spatial regions of homogeneous or continuous flowappearance. E.g., the flow of an approaching fronto-parallel flatsurface resembles a radially expanding vector field which can beidentified in the image. In a similar way, approaching cars can besegmented by their characteristic flow field.

In a second aspect, a sensor system comprising at least one visualsensor is provided, comprising:

-   -   means for supplying a sequence of images is provided,    -   means for obtaining, from the visual sensor, at least two images        of the sequence of images,    -   optional means for detecting whether the images include a        distortion,    -   means for determining an image warping function that at least        partially compensates a distortion in at least one of the        images,    -   processing means for applying the determined warping function to        the at least one image, for at least partially compensating the        distortion by modifying the image, and for calculating and        outputting an optical flow as a displacement vector field form        modified image and another image of the at least two images.

Optionally the system can include means for detecting whether one of theat least two images includes a distortion.

The optical flow can be corrected by subtracting shifts introduced dueto the warping function application.

The processing can calculate and apply, in case the image is distorted,the image warping function to the image including the distortion, theimage warping function being calculated to compensate the distortion.

The system can perform the method as outlined above.

In yet another aspect, the invention provides a land, air, sea or spacevehicle equipped with a sensor system outlined above.

The vehicle may be a robot or a motorcycle, a scooter, otherwheeled-vehicle, a passenger car or a lawn mower.

In still another aspect, the invention provides a vehicle driverassistance system including the sensor system as defined above.

In another aspect the invention provides a computer program productperforming, when executed on a computer, the method outlined above.

The invention is now also described with reference to the figures. Inparticular,

FIG. 1 shows exemplarily and schematically an autonomous vehicle;

FIG. 2 shows an exemplary first image and a second image, wherein thesecond image results from a non-translational transformation of thefirst image;

FIG. 3 shows on the top the images of FIG. 2 and below, for the secondimage, a warped version of this image.

FIG. 4 schematically depicts key method steps of the invention.

The aim of the invention is to improve optical flow estimationperformance in a visual sensor system, especially a camera-based streamrecording system. In particular, the visual sensor system is part of avehicle and more particular an autonomous or partially autonomousvehicle, which is schematically and exemplarily shown in FIG. 1.

For estimating the optical flow, it is necessary to find correlatingimage pixels between a temporally previous (“first”) and a temporallyfollowing (“second”) recorded image. The pixel correlations arecalculated using a certain search range, i.e., starting from a pixelposition x,y in the first image and searching in the proximity of thesame position in the second image. Since the cost of the optical flowcalculation depends on the size of the search range, smaller searchranges are beneficial.

The most basic assumption is that the patch in the second image has thesame pixel values, but is a shifted version of the patch from the firstimage, so that the pixels around x,y from the first image exhibit thesame structure (in terms of the conformation of the pixel values aroundx,y and x′,y′) as the pixels around x′,y′ from the second image.

If the patch has a rectangular shape, these approaches are called blockmatching approaches. The resulting displacement vectors x′-x, y′-y foreach pixel position then constitute the optical flow field whichindicates which pixels have moved to which new positions from the firstto the second image.

The optical flow field can then be analyzed further for object motion bysearching for areas that have a coherent flow of a certain form, suchas, e.g., a coherent flow field (same flow vector at all positions ofthe object) of an object that translates over the field of view. Thequality of the optical flow is a very important factor for the qualityof the ego-motion determination but also for the detection of movingobjects in the movement path e.g. of a vehicle, and/or an estimation ofobject motion.

For the optical flow calculation, the translational transformationbetween two images is dealt with by the search range within whichpatches are compared. Usually, it is assumed that the structure of thecorresponding patches in the first and the second images remains thesame, i.e., that the patches are translated versions of each other.

However, sometimes the image patches are not translated versions of eachother. Especially when one of the images undergoes a non-translational,such as e.g. a rotational transformation, the standard patch-matchtechniques fail because also the patches are not translationallytransformed versions of each other.

This is exemplarily shown in FIG. 2 for the case of an image rotation.

At the top of FIG. 2, two rotated versions of an image can be seen, asit may occur if the camera rotates around its optical axis. The secondimage is rotated by 90° counter clockwise with respect to the firstimage. At the bottom of FIG. 2, a center patch is shown. A pixel-basedcorrelation of the patches fails because the patches are rotatedversions of each other.

By applying a general image warp function to either the first or thesecond image prior to the actual patch-based correspondence searchcompensates for the patch rotation and allows the patch-basedcorrespondence search to succeed. The image warping at least reduces theeffect of perspective or ego-motion induced appearance changes in orderto prevent wrong or failing correlations in patch-based correspondencesearches (choices for and selection options of the warping function aredescribed below).

Warping an image means that a geometrical distortion is applied to theimage so that the warped image is a distorted version of the non-warpedimage. In mathematical terms, this is accomplished by applying, to thepixel values I(x,y) a functionI ^(warped)(x ^(T) ,y ^(T))=I(x,y)so that the pixel values of the warped image I^(warped) at transformedpositions x^(T),y^(T) are taken from the pixel values from the unwarpedimage I at the original positions x,y, where the position transformationis given by a function ω (often a linear/affine function) withx ^(T)=ω_(x)(x,y)y ^(Y)=ω_(y)(x,y).

In FIG. 3, the two bottom images now show an exemplary comparisonbetween the first image and the warped second image (which in this caseis rotated back by) 90°, and their corresponding patch comparison.

Resulting optical flow values (displacement vectors) of the optical flowresulting from the patch comparison of the first image with the warpedsecond image are influenced by the warping, so that the warping effecthas to be subtracted from the calculated optical flow values at eachposition x, y. This is done by subtracting the shift due to warpingx^(T)−x,y^(T)−y from the flow field {right arrow over (v)}(x,y) so thatthe final, correct flow field isv _(x) ^(final) =v _(x)−(x ^(T) −x)v _(y) ^(final) =v _(y)−(y ^(T) −y)

The source of the warping model for optical flow calculation can bemanifold. One straightforward way is to estimate which warping should beused by using additional sensors that detect the camera motion, such asin the explained case of camera rotation. I.e. a sensor providesinformation on how the camera is/was moved, which is indicative of thewarping required for compensating the distortion resulting from themovement in the captured image. Another way is to estimate simpleparametric warping models (like affine models or homographies) directlyfrom the unwarped images or even from images of preceding time stepswith the aid of motion estimation.

An easy way of estimating a warping function is a fixed assumption onthe scenario recorded by the image sensors (e.g. a vehicle moving on aground plane with a constant speed); from this knowledge a warpingfunction can be derived by calculating the theoretically expected flow.

One issue that needs consideration is that the image warping mightdeteriorate the spatial relations of pixels that do not follow thewarping model. In this case, e.g. the warping model could be applied tothe part of the image where the model is expected to fit.

An example is again the optical flow generated when a vehicle moves on aground plane; in this case an expected warping model for the groundplane can be derived but it applies only on the portion of the imagewhere the ground plane is visible, so that the warping model can beexpected to fit in the image areas below the horizon line but not aboveit (e.g. in the sky). Whether a model fits can be judged directly fromthe resulting values of the patch comparison at each pixel position x,y.

Another option is to compute multiple optical flow calculations usingdifferent warping models and combining the different corrected opticalflow results into a single integrated result by a pixel-based selectionof the most suitable/best one (suitability here can e.g. be quantifiedby a confidence value or the matching score). It should be noted thatone instance of the multiple warping models can be a null model (i.e.,the original images without warping).

The integration of the multiple optical flow results from differentwarping models into a combined optical flow can be achieved byselecting, for each pixel, the flow with the highest confidence valuerespectively the best correlation value.

Altogether the invention for optical flow can be summarized in thefollowing method:

-   -   First, images (“first” and “second”) are acquired from a camera.    -   Second, at least one of the camera images (e.g. the second        image) is warped according to a warping model, by application of        an image warping function.    -   Third, a standard patch-based optical flow computing algorithm        is used to compute a displacement field from the first to the        second image (of which one is warped).    -   Fourth, the displacement field is corrected by subtracting the        shifts that were introduced due to the warping of the second        image.

FIG. 4 depicts these basic steps as a flow chart.

In summary, for the optical flow estimation based application domains,this invention allows for determining more accurate optical flow fieldscompared to normal optical flow processing without warping the images.This is achieved by correcting for occurring image transformations thatare caused e.g. by the motion of the cameras and which affects theprocess for finding correlations. Thus, the presented invention improvesthe accuracy of optical flow estimates as well as subsequent processingstages like moving object detection.

The calculation of optical flow can be achieved using a patch-basedmatching method. The patch-based-matching method can use the sum ofnormalized cross-correlations as a correlation measure(cross-correlations are e.g. defined in EP 10 162 822). The warpingmodel can be estimated from camera motion information. The camera imagescan be additionally or alternatively corrected for lens distortion. Thisis also performed to improve the optical flow. A predefined warping canbe applied to compensate for lens distortion. Also, more than onewarping may be used to warp the one camera image (e.g. a warping forlens and a warping for motion distortion, and/or multiple warps tocompensate a distortion).

The method can be used multiple times each with a different model andthe corrected optical flow results are combined into one optical flowresult by selecting the best flow for each pixel as described, using aconfidence measure as e.g. the correlation value.

Additionally to the computation of the optical flow from the warpedimages, a computation of an optical flow from the unwarped camera imagescan be performed where both results are merged into a final resultselecting the best value for each pixel. The best optical flow value canbe the one having a better correlation value. Correlation values areusually calculated between two patches using state-of-the art normalizedcross-correlation or similar.

The warping model for the current image frame can be estimated from thelast optical flow image or from a set of optical flow images from thelast time steps (an example of how to do this is to e.g. choose a linearposition transformation function ω and to estimate its parameters bydemanding that the gained optical flow should resemble closely theoptical flow from the last time steps according to some distancefunction, e.g. Euclidean distance). The warping model can be given apriori and fixed. The warping model can be an affine model. The warpingmodel can be a general nomography. The optical flow results can be usedto detect obstacles or moving objects in the scene observed by thecamera or cameras.

Possible other applications are driver assistant systems like collisionwarning, or cruise control. For example the improvement of moving objectdetection allows a better estimation of braking and evasion maneuvers aswell as a better calculation of the optimal driving path. Anotherapplication field is in robotic systems, where the improved optical flowestimation is used for object detection and object motion estimation.

Another application is an autonomous lawn mower. Here the improvedperception of moving objects allows for a predictive path planning thatdoes not harm or annoy garden users.

Further, the invention can also be applied in various other domains, oneof them being robotics but as well applied in systems for ground, waterand/or air bound vehicles, generally including systems designed toassist a human operator. The method and system disclosed herein ingeneral may be used whenever a technical (e.g., an electronic) system isrequired to autonomously deal with features occurring in a movement pathobserved and/or properties of objects (e.g., size, distance,relative/absolute position also to other objects, spatial alignment,relative movement, speed and/or direction and other related objectfeatures or feature patterns) which are presented to the system.

In order to process obtained information (observations), the inventionmay use and include analysis means employing the processing module 3and/or apply neural networks, which can generally be used to inferfunctions from observations. Neural networks allow working with none oronly little a priori knowledge on a problem to be solved and also show afailure tolerant behavior. Problems that may be addressed relate, e.g.,to feature identification, control (vehicle control, process control),decision making, machine vision and/or pattern recognition (facialrecognition, object recognition, gesture recognition, speechrecognition, character and text recognition), etc. A neural networkthereby consists of a set of neurons and a set of synapses. The synapsesconnect neurons and store information in parameters called weights,which are used in transformations performed by the neural network andlearning processes.

Typically, to make an observation, an input signal or input pattern isaccepted from the detection means 2 which is processed using hardwareunits and/or software components. An output signal or output pattern isobtained, which may serve as input to other systems for furtherprocessing, e.g. for visualization purposes.

The input signal, which may also include information on detectedfeatures influencing movement, may be supplied by one or more sensors,e.g. the mentioned visual detecting means 2, but also by a software orhardware interface. The output pattern may as well be output through asoftware and/or hardware interface or may be transferred to anotherprocessing module 3 or actor, e.g. a powered steering control or a brakecontroller, which may be used to influence the actions or behavior ofthe vehicle.

Computations and transformations required by the invention, necessaryfor evaluation, processing, maintenance, adjustment, and also execution(e.g. of movement change commands or actuation commands) may beperformed by a processing module 3 such as one or more processors(CPUs), signal processing units or other calculation, processing orcomputational hardware and/or software, which might also be adapted forparallel processing. Processing and computations may be performed onstandard off the shelf (OTS) hardware or specially designed hardwarecomponents. A CPU of a processor may perform the calculations and mayinclude a main memory (RAM, ROM), a control unit, and an arithmeticlogic unit (ALU). It may also address a specialized graphic processor,which may provide dedicated memory and processing capabilities forhandling the computations needed.

Also data memory is usually provided. The data memory is used forstoring information and/or data obtained, needed for processing,determination and results. The stored information may be used by otherprocessing means, units or modules required by the invention. The memoryalso allows storing or memorizing observations related to events andknowledge deducted therefrom to influence actions and reactions forfuture events.

The memory may be provided by devices such as a hard disk (SSD, HDD),RAM and/or ROM, which may be supplemented by other (portable) memorymedia such as floppy disks, CD-ROMs, Tapes, USB drives, Smartcards,Pendrives etc. Hence, a program encoding a method according to theinvention as well as data acquired, processed, learned or needed in/forthe application of the inventive system and/or method may be stored in arespective memory medium.

In particular, the method described by the invention may be provided asa software program product on a (e.g., portable) physical memory mediumwhich may be used to transfer the program product to a processing systemor a computing device in order to instruct the system or device toperform a method according to this invention. Furthermore, the methodmay be directly implemented on a computing device or may be provided incombination with the computing device.

It should be understood that the foregoing relates not only toembodiments of the invention and that numerous changes and modificationsmade therein may be made without departing from the scope of theinvention as set forth in the following claims.

The invention claimed is:
 1. A method for processing a sequence ofimages, comprising: obtaining, from an optical sensor, at least twoimages of the sequence of images, determining an image warping functionthat at least partially compensates a distortion in at least one of thetwo images, applying the determined image warping function to the atleast one image, at least partially compensating the distortion bymodifying the at least one image, and calculating and outputting anoptical flow as a displacement vector field from the modified at leastone image and another image of the at least two images, wherein theoptical flow is corrected by subtracting shifts introduced due to thewarping function application.
 2. The method according to claim 1,further comprising a step of detecting whether one of the at least twoimages includes a distortion.
 3. The method according to claim 1,wherein the optical flow is calculated by matching patches of one imageto the other.
 4. The method according to claim 1, wherein the warpingfunction is determined from assumptions on the scene that is recorded bythe optical sensor and the knowledge about movement of a visual system.5. The method according to claim 1, wherein the warping function isdetermined from motion information supplied by a sensor of a visualsystem and indicative of a motion the distortion results from.
 6. Themethod according to claim 1, wherein the distortion comprises a motiondistortion or a lens distortion.
 7. The method according to claim 1,wherein more than one image warping function is applied to the at leastone image including the distortion.
 8. The method according to claim 1,wherein the image warping function is applied to one of the at least twoimages.
 9. The method according to claim 1, wherein the method isperformed more than once, each time with a different image warpingfunction on the same images and the optical flow results are combinedinto one optical flow.
 10. The method according to claim 1, wherein inaddition to the calculation of the optical flow from the images withapplication of the image warping function, calculating a second opticalflow from the images without application of the image warping functionand combining both optical flow results.
 11. The method according toclaim 1, wherein the image warping function is determined from aprevious optical flow calculation.
 12. The method according to claim 1,wherein the image warping function is a predetermined and fixed imagewarping function, an affine warping function, or a homography.
 13. Themethod according to claim 1, wherein objects and/or moving objects inthe images are detected based on the optical flow.
 14. A sensor systemcomprising at least an optical sensor configured to supply a sequence ofimages, the system comprising: means for obtaining from the opticalsensor, at least two images of the sequence of images, means fordetermining an image warping function that at least partiallycompensates a distortion in at least one of the two images, means forapplying the determined image warping function to the at least oneimage, and for at least partially compensating the distortion bymodifying the at least one image, and means for calculating andoutputting, an optical flow as a displacement vector field from themodified at least one image and another image of the at least twoimages, wherein the optical flow is corrected by subtracting shiftsintroduced due to the warping function application.
 15. The system ofclaim 14, wherein the means for applying is configured to calculate andto apply, in case the at least one image is distorted, the at least oneimage warping function to the image including the distortion, the imagewarping function being calculated to compensate the distortion and/orcomprising a motion sensor indicating motion of the optical sensor. 16.A sensor system comprising at least one optical sensor configured toobtain a sequence of images, the sensor system comprising: an obtainingdevice for obtaining, from the optical sensor, at least two images ofthe sequence of images; a determining device configured to determine animage warping function that at least partially compensates a distortionin at least one of the two images; an applying device configured toapply the determined image warping function to the at least one image,and configured to at least partially compensate the distortion bymodifying the at least one image; and a calculating and outputtingdevice configured to calculate and output an optical flow as adisplacement vector field from the at least one modified image andanother image of the at least two images, wherein the optical flow iscorrected by subtracting shifts introduced due to the warping functionapplication.
 17. A land, air, sea or space vehicle equipped with thesensor system according to claim
 14. 18. The vehicle according to claim17, wherein the vehicle comprises a robot or a motorcycle, a scooter,other 2-wheeled vehicle, a passenger car or a lawn mower.
 19. A vehicledriver assistance system including the sensor system according to claim14.
 20. A computer program product embodied on a non-transitorycomputer-readable medium, said computer program product comprising codethat, when executed on a computer, controls the computer to perform themethod according to claim
 1. 21. The method according to claim 9,wherein the one optical flow comprises a single displacement vectorfield.